Monday February 28, 2022
1:00PM Eastern Time
Instructor, Johns Hopkins University, School of Medicine
Department of Oncology, Division of Biostatistics and Bioinformatics;
Department of Neuroscience; and McKusick-Nathans Department of Genomic Medicine
Assistant Director, Johns Hopkins University Single Cell Consortium
Abstract: As the single-cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multiomics analysis. Merged with statistical and mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps. Thus, systems biology for forecasting biological system dynamics from multiomic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.